
jupyter_pwd = %pwd
if jupyter_pwd == "/":
    %cd /workspace


# ipynb形式のライブラリのインポート
%run ./lib/lib.ipynb

# ipynb形式のライブラリノートを.py形式に変更したものをインポート
import lib
import lib.lab_lib
from lib.lab_lib import *

# 生データの入ったCSVファイルの保持されたディレクトリ名を格納している変数
csvDirPath = "./csv_files/"

# NPBのベンチマーク名のリスト
benchmarkNames = ["cg", "ep", "ft", "is", "lu", "mg"]

# NPBのプロセス数
npb_process :list[int] = [2, 4, 8, 16, 32, 64, 128, 256]
train_npb_process :list[int] = npb_process[:-1]
test_npb_process :list[int] = npb_process[-1:]
# NPBのCGの初期変数
cg_na: list[int] = [14000, 30000, 75000, 100000, 1500000]
cg_nonzer: list[int] = [11, 12, 13, 14, 15, 18, 21]
cg_niter: list[int] = [15, 30, 75, 90, 100]
cg_shift: list[int] = [20, 40, 60, 80, 110, 200]

train_cg_na: list[int] = cg_na[:-1]
train_cg_nonzer: list[int] = cg_nonzer[:-1]
train_cg_niter: list[int] = cg_niter[:-1]
train_cg_shift: list[int] = cg_shift[:-1]

test_cg_na: list[int] = cg_na[-1:]
test_cg_nonzer: list[int] = cg_nonzer[-1:]
test_cg_niter: list[int] = cg_niter[-1:]
test_cg_shift: list[int] = cg_shift[-1:]

# LULESH ベンチマークプログラムのプロセス数・問題サイズ・イテレーション数
lulesh_processes: list[int] = [8, 27, 64, 125, 216, 343, 512]
lulesh_iterations: list[int] = [8, 16, 32, 64, 128, 256]
lulesh_sizes: list[int] = [16, 24, 32, 48, 64, 128]

train_lulesh_processes: list[int] = [8, 27, 64, 125, 216, 343]
train_lulesh_iterations: list[int] = [8, 16, 32, 64, 128]
train_lulesh_sizes: list[int] = [16, 24, 32, 48]

test_lulesh_processes: list[int] = [512, 729, 1000]
test_lulesh_iterations: list[int] = [256, 512, 1024]
test_lulesh_sizes: list[int] = [64, 96, 128]


list_modelName: list[str] = [
    "modelIp",
    "modelLog",
    "modelLinAndIp",
    "modelLinAndLog",
    "modelIpAndLin",
    "modelIpAndLog",
    "modelLogAndLin",
    "modelLogAndIp",
    "modelProcessDividedByProblemSize",
    "modelProblemSizeDividedByProcess",
    "modelInfiniteProductOfProblemSizeMultipliedByProcesses",
    "modelInfiniteProductOfProblemSizeDividedByProcesses",
    "modelLinearSumOf2elementCombination",
    "modelLinearSumOfElementCombinations",
    "modelLinearSumOf2elementCombinationWithSquared",
    "modelLinearSumOf2elementCombinationWithCubed",
    "modelSquareRootOfProcess",
    "modelSquareRootTimesOtherElems",
    "modelObeyOneParameter",
    "modelLin"
    # "modelBasicTree",
]
list_csvDir = [
    "./csv_files/lulesh_1st/",
    "./csv_files/lulesh_2nd/",
    "./csv_files/lulesh_3rd/",
]

/workspace

DEBUG:__main__:hello
DEBUG:lib.lab_lib:hello


list_series: list[pd.Series] = []
pis: list[str]
for elem_process in [8, 27, 64, 125, 216, 343] + [512, 729, 1000]:
    print(elem_process)

    fileDir: str = "./txt_files/ElapFiles/"
    fileName: str = f"p{elem_process}_Elap"
    with open(fileDir + fileName) as f:
        l: list[str] = [s.strip() for s in f.readlines()]
    if len(l) % 2 != 0:
        warnings.warn("ファイルの行数が偶数ではありません")
    for i in range(len(l)):
        if i % 2 == 0:
            pis = l[i].split(sep="/")[-3:]
        else:
            time = l[i].split(sep=" ")[-2]
            _series: pd.Series = pd.Series(
                {
                    "p": int(pis[0].replace("p", "")),
                    "i": int(pis[1].replace("i", "")),
                    "s": int(pis[2].replace("s", "")),
                    "time": float(time),
                }
            )
            _series["cost"] = _series["p"] * _series["time"]
            list_series.append(_series)
        # print(f"pis={pis}, time={time}")

DF_pis_time: pd.DataFrame = pd.DataFrame(data=list_series)

DF_pis_time

8
27
64
125
216
343
512
729
1000


target_p_border: int = test_lulesh_processes[0]
target_i_border: int = test_lulesh_iterations[0]
target_s_border: int = test_lulesh_sizes[0]

DF_pis_time_at_train: pd.DataFrame = DF_pis_time[
    (DF_pis_time["p"] < target_p_border)
    & (DF_pis_time["i"] < target_i_border)
    & (DF_pis_time["s"] < target_s_border)
]
DF_pis_time_at_target: pd.DataFrame = DF_pis_time[
    (DF_pis_time["p"] >= target_p_border)
    & (DF_pis_time["i"] >= target_i_border)
    & (DF_pis_time["s"] >= target_s_border)
]


cost_to_build_model: float = sum(DF_pis_time_at_train["cost"])


DF_pis_time_at_target = DF_pis_time_at_target.sort_values(by=["p", "i", "s"])
DF_pis_time_at_target


DF_pis_time_at_train = DF_pis_time_at_train.sort_values(by=["p", "i", "s"])
condition_col: list[str] = ["p", "i", "s"]
averaged_DF_pis_time_at_target = DF_pis_time_at_target.groupby(condition_col).aggregate(
    np.mean
)
DF_pis_time_at_train


import plotly.graph_objs as go

trace_data = []
layout = go.Layout(barmode="stack")
for i, sr in DF_pis_time_at_target.reset_index().iterrows():
    x = [j for j in range(1, len(DF_pis_time_at_target) + 1)]
    y = [0] * len(DF_pis_time_at_target)
    for k in range(int(i), len(DF_pis_time_at_target)):
        y[k] = sr["cost"]
    trace = go.Bar(x=x, y=y, name=f"p{int(sr['p'])}i{int(sr['i'])}s{int(sr['s'])}")
    trace_data.append(trace)

fig = go.Figure(data=trace_data, layout=layout)
fig.add_shape(
    type="line",
    x0=0,
    x1=len(DF_pis_time_at_target),
    y0=cost_to_build_model,
    y1=cost_to_build_model,
    line=dict(
        color="Red",
    ),
    xref="x",
    yref="y",
    name="cost to build model",
)
fig.update_layout(
    xaxis=dict(title="counts of target environments"),
    yaxis=dict(title="cost"),
)
fig.show()


averaged_DF_pis_time_at_target = averaged_DF_pis_time_at_target.reset_index()


graph_x: list[int] = list(range(1, len(averaged_DF_pis_time_at_target) + 1))
graph_y: list[float] = [0] * len(averaged_DF_pis_time_at_target)

for i, sr in averaged_DF_pis_time_at_target.iterrows():
    for k in range(i, len(averaged_DF_pis_time_at_target)):
        graph_y[k] += sr["cost"]


data = go.Scatter(x=graph_x, y=graph_y)
fig = go.Figure(data=data)
fig.add_shape(
    type="line",
    x0=0,
    x1=len(averaged_DF_pis_time_at_target),
    y0=cost_to_build_model,
    y1=cost_to_build_model,
    line=dict(
        color="Red",
    ),
    xref="x",
    yref="y",
    name="cost to build model",
)
fig.update_layout(
    xaxis=dict(title="Number of scales predicted"),
    yaxis=dict(title="cost", type="log"),
    width=1000,
    height=500,
)

fig.show()


trace1 = go.Bar(x=["giraffes", "orangutans", "monkeys"], y=[20, 14, 23], name="SF Zoo")
trace2 = go.Bar(x=["giraffes", "orangutans", "monkeys"], y=[21, 18, 29], name="LA Zoo")
trace3 = go.Bar(x=["giraffes", "orangutans", "monkeys"], y=[22, 19, 30], name="LA Zoo")

data = [trace1, trace2, trace3]
layout = go.Layout(barmode="stack")

fig = go.Figure(data=data, layout=layout)
fig.show()


resVar = "Exclusive"


costToBuildModel: float = 0
for elem_process in train_lulesh_processes:
    for elem_iteration in train_lulesh_iterations:
        for elem_size in train_lulesh_sizes:
            inputDFtoGetCost: pd.DataFrame = ret_averaged_rawDF_lulesh(
                list_process=[elem_process],
                list_iteration=[elem_iteration],
                list_size=[elem_size],
                list_csvDir=list_csvDir,
                resVar=resVar,
            )
            costToBuildModel += get_CostAtInputDF(
                inputDF=inputDFtoGetCost,
                targetColName=resVar,
                numOfProcess=elem_process,
            )

print(f"costToBuildModel={costToBuildModel}")

costToBuildModel=79575.00722467963


list_toDFaboutCostAndCondition: list[pd.Series] = []
for elem_process in test_lulesh_processes:
    for elem_iteration in test_lulesh_iterations:
        for elem_size in test_lulesh_sizes:
            inputDFtoGetCost: pd.DataFrame = ret_averaged_rawDF_lulesh(
                list_process=[elem_process],
                list_iteration=[elem_iteration],
                list_size=[elem_size],
                list_csvDir=list_csvDir,
                resVar=resVar,
            )
            costInThisCondition: float = get_CostAtInputDF(
                inputDF=inputDFtoGetCost,
                targetColName=resVar,
                numOfProcess=elem_process,
            )
            _series: pd.Series = pd.Series(
                {
                    "cost": costInThisCondition,
                    "process": elem_process,
                    "iteration": elem_iteration,
                    "size": elem_size,
                    "relativeCost(=costToBuildModel/cost * 100)": costToBuildModel
                    / costInThisCondition
                    * 100,
                }
            )
            list_toDFaboutCostAndCondition.append(_series)

DFaboutCost: pd.DataFrame = pd.DataFrame(data=list_toDFaboutCostAndCondition)

DFaboutCost


print(DFaboutCost.to_csv())

,cost,process,iteration,size,relativeCost(=costToBuildModel/cost * 100)
0,67660.44779434668,512.0,256.0,64.0,117.60934167410058
1,241655.66206907728,512.0,256.0,96.0,32.92908866415599
2,585628.5268031145,512.0,256.0,128.0,13.587966361384643
3,134829.38787379197,512.0,512.0,64.0,59.019037673868546
4,489347.61192789325,512.0,512.0,96.0,16.261447953363106
5,1175641.4546334718,512.0,512.0,128.0,6.768645909095527
6,274479.7778389333,512.0,1024.0,64.0,28.99120942577228
7,968032.1129782613,512.0,1024.0,96.0,8.220285893187782
8,2344354.329343829,512.0,1024.0,128.0,3.3943250910775173
9,104297.04679624198,729.0,256.0,64.0,76.29651046605365
10,346765.65203209495,729.0,256.0,96.0,22.947776620423337
11,833179.7265746038,729.0,256.0,128.0,9.550761340753098
12,201359.75723555396,729.0,512.0,64.0,39.518823580816836
13,701192.085256251,729.0,512.0,96.0,11.348531864217906
14,1669530.079918935,729.0,512.0,128.0,4.766311681460897
15,393842.233052451,729.0,1024.0,64.0,20.204792819687782
16,1400878.763300601,729.0,1024.0,96.0,5.680363591007226
17,3355711.696609109,729.0,1024.0,128.0,2.371330269673907
18,140086.98776966662,1000.0,256.0,64.0,56.80399621092446
19,490789.0369463333,1000.0,256.0,96.0,16.21368882234853
20,1160934.3817936662,1000.0,256.0,128.0,6.8543931916061185
21,277380.9845486667,1000.0,512.0,64.0,28.68798211029428
22,988088.7108259998,1000.0,512.0,96.0,8.053427425373409
23,2316158.048016,1000.0,512.0,128.0,3.435646686237274
24,562736.3166596666,1000.0,1024.0,64.0,14.140727169880748
25,1943520.7169620001,1000.0,1024.0,96.0,4.094374015681536
26,4651948.381511664,1000.0,1024.0,128.0,1.7105737359626827


expVar: list[str] = ["process", "iteration", "size"]
resVar: str = "Exclusive"

trainDF: pd.DataFrame = ret_averaged_rawDF_lulesh(
    list_process=train_lulesh_processes,
    list_iteration=train_lulesh_iterations,
    list_size=train_lulesh_sizes,
    list_csvDir=list_csvDir,
    resVar=resVar,
)

testDF: pd.DataFrame = ret_averaged_rawDF_lulesh(
    list_process=test_lulesh_processes,
    list_iteration=test_lulesh_iterations,
    list_size=test_lulesh_sizes,
    list_csvDir=list_csvDir,
    resVar=resVar,
)

testDF = testDF.reset_index()
trainDF = trainDF.reset_index()

testDF["functionName"] = testDF["Name"]
trainDF["functionName"] = trainDF["Name"]

functionNames: list[str] = sorted(list(set(trainDF["Name"])))

dict_symbols = {}
for elem in expVar:
    dict_symbols[elem] = symbols(elem, real=True)

target_env = [
    (dict_symbols["size"], test_lulesh_sizes[-1]),
    (dict_symbols["iteration"], test_lulesh_iterations[-1]),
    (dict_symbols["process"], test_lulesh_processes[-1]),
]

filePath: str = f"./extra-p_docker/share/input_lulesh_perFunc.txt"


dict_models: dict[str, dict[str, any]] = {}
for functionName in functionNames:

    trainDF_perFunc: pd.DataFrame = trainDF[trainDF["Name"] == functionName]
    testDF_perFunc: pd.DataFrame = testDF[testDF["Name"] == functionName]

    # 総実行時間

    resVar = "Exclusive"
    # Extra-P への入力ファイル作成
    str_ExtraPinputData: str = gen_ExtraPinputDataFromDF(
        inputDF=testDF_perFunc,
        expVar=expVar,
        resVar=resVar,
    )
    with open(filePath, mode="w") as f:
        f.write(str_ExtraPinputData)
    # Extra-P の実行とその出力の取得
    res_str: str = subprocess.run(
        "extrap --text ./extra-p_docker/share/input_lulesh_perFunc.txt | grep Model",
        stdout=subprocess.PIPE,
        text=True,
        shell=True,
    ).stdout
    res_str_default: str = subprocess.run(
        "extrap --text ./extra-p_docker/share/input_lulesh_perFunc.txt --modeler default --options poly_exponents=-1,0,1,2,3 log_exponents=0,1 force_combination_exponents=1 allow_negative_exponents=1 | grep Model",
        stdout=subprocess.PIPE,
        text=True,
        shell=True,
    ).stdout
    res_str_multiParameter: str = subprocess.run(
        "extrap --text ./extra-p_docker/share/input_lulesh_perFunc.txt --modeler multi-parameter | grep Model",
        stdout=subprocess.PIPE,
        text=True,
        shell=True,
    ).stdout

    # 取得した Extra-P の出力の整形
    res_str = res_str.replace("Model: ", "")
    res_str = convert_log(res_str)
    res_str_default = res_str_default.replace("Model: ", "")
    res_str_default = convert_log(res_str_default)
    res_str_multiParameter = res_str_multiParameter.replace("Model: ", "")
    res_str_multiParameter = convert_log(res_str_multiParameter)
    # 総実行時間の予測
    model_sympy_all = sympify(res_str, locals=dict_symbols)
    model_sympy_all_default = sympify(res_str_default, locals=dict_symbols)
    model_sympy_all_multiParameter = sympify(
        res_str_multiParameter, locals=dict_symbols
    )

    # 1コール当たりの実行時間

    resVar = "ExclusivePerCall"
    # Extra-P への入力ファイル作成
    str_ExtraPinputData: str = gen_ExtraPinputDataFromDF(
        inputDF=testDF_perFunc,
        expVar=expVar,
        resVar=resVar,
    )
    with open(filePath, mode="w") as f:
        f.write(str_ExtraPinputData)
    # Extra-P の実行とその出力の取得
    res_str: str = subprocess.run(
        "extrap --text ./extra-p_docker/share/input_lulesh_perFunc.txt | grep Model",
        stdout=subprocess.PIPE,
        text=True,
        shell=True,
    ).stdout
    res_str_default: str = subprocess.run(
        "extrap --text ./extra-p_docker/share/input_lulesh_perFunc.txt --modeler default --options poly_exponents=-1,0,1,2,3 log_exponents=0,1 force_combination_exponents=1 allow_negative_exponents=1 | grep Model",
        stdout=subprocess.PIPE,
        text=True,
        shell=True,
    ).stdout
    res_str_multiParameter: str = subprocess.run(
        "extrap --text ./extra-p_docker/share/input_lulesh_perFunc.txt --modeler multi-parameter | grep Model",
        stdout=subprocess.PIPE,
        text=True,
        shell=True,
    ).stdout

    # 取得したExtra-Pの出力の整形
    res_str = res_str.replace("Model: ", "")
    res_str = convert_log(res_str)
    res_str_default = res_str_default.replace("Model: ", "")
    res_str_default = convert_log(res_str_default)
    res_str_multiParameter = res_str_multiParameter.replace("Model: ", "")
    res_str_multiParameter = convert_log(res_str_multiParameter)
    # 総実行時間の予測
    model_sympy_perCall = sympify(res_str, locals=dict_symbols)
    model_sympy_perCall_default = sympify(res_str_default, locals=dict_symbols)
    model_sympy_perCall_multiParameter = sympify(
        res_str_multiParameter, locals=dict_symbols
    )

    # 関数コール回数の予測

    # 関数コール回数予測のためのモデルを構築
    resVar = "#Call"
    bestModelDict: dict = return_bestModelObject(
        inputDF=trainDF_perFunc,
        list_expVar=expVar,
        list_resVar=[resVar],
        list_modelName=list_modelName,
    )
    bestModel = bestModelDict["object"]

    dict_models_perFunc: dict[str, any] = {
        "time_all": model_sympy_all,
        "time_all_default": model_sympy_all_default,
        "time_all_multiParameter": model_sympy_all_multiParameter,
        "time_perFunc": model_sympy_perCall,
        "time_perFunc_default": model_sympy_perCall_default,
        "time_perFunc_multiParameter": model_sympy_perCall_multiParameter,
        "call_perFunc": bestModel,
    }
    dict_models[functionName] = dict_models_perFunc

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
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Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

/usr/local/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:881: OptimizeWarning:

Covariance of the parameters could not be estimated

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]


# 集計をする
# それぞれの予測対象ケースで総実行時間の相対誤差率を算出する
# 算出された相対誤差率をプロットする

resVar: str = "Exclusive"
dict_resultDF_perFunc = {}
for elem_process in test_lulesh_processes:
    for elem_iteration in test_lulesh_iterations:
        for elem_size in test_lulesh_sizes:

            print(
                f"process={elem_process}, iteration={elem_iteration}, size={elem_size}"
            )

            testDF_oneCase: pd.DataFrame = ret_averaged_rawDF_lulesh(
                list_process=[elem_process],
                list_iteration=[elem_iteration],
                list_size=[elem_size],
                list_csvDir=list_csvDir,
                resVar=resVar,
            )
            testDF_oneCase["functionName"] = testDF_oneCase["Name"]

            target_env = [
                (dict_symbols["size"], elem_size),
                (dict_symbols["iteration"], elem_iteration),
                (dict_symbols["process"], elem_process),
            ]

            functionNames: list[str] = sorted(list(testDF_oneCase["Name"]))
            list_series: list[pd.Series] = []
            for functionName in functionNames:
                testDF_oneCase_perFunc: pd.DataFrame = testDF_oneCase[
                    testDF_oneCase["Name"] == functionName
                ]

                if len(testDF_oneCase_perFunc) != 1:
                    warnings.warn("len(testDF_oneCase_perFunc) != 1")
                    exit

                # 総実行時間の予測
                Exclusive_predicted_all: float = (
                    dict_models[functionName]["time_all"].subs(target_env).evalf()
                )
                Exclusive_predicted_all_default: float = (
                    dict_models[functionName]["time_all_default"]
                    .subs(target_env)
                    .evalf()
                )
                Exclusive_predicted_all_multiParameter: float = (
                    dict_models[functionName]["time_all_multiParameter"]
                    .subs(target_env)
                    .evalf()
                )
                # 1コール当たりの実行時間
                Exclusive_predicted_perFunc: float = (
                    dict_models[functionName]["time_perFunc"].subs(target_env).evalf()
                )
                Exclusive_predicted_perFunc_default: float = (
                    dict_models[functionName]["time_perFunc_default"]
                    .subs(target_env)
                    .evalf()
                )
                Exclusive_predicted_perFunc_multiParameter: float = (
                    dict_models[functionName]["time_perFunc_multiParameter"]
                    .subs(target_env)
                    .evalf()
                )
                # コール回数の予測
                predicted_call: float = float(
                    np.array(
                        dict_models[functionName]["call_perFunc"].predict(
                            inputDF=testDF_oneCase_perFunc[expVar]
                        )
                    )
                )

                # 実測値の取得
                Exclusive_real_time: float = testDF_oneCase_perFunc.reset_index().loc[
                    0
                ][resVar]
                real_call: float = testDF_oneCase_perFunc.reset_index().loc[0]["#Call"]

                _series: pd.Series = pd.Series(
                    {
                        "functionName": functionName,
                        f"{resVar}_real_time": Exclusive_real_time,
                        f"{resVar}_predicted_all": Exclusive_predicted_all,
                        f"{resVar}_predicted_all_default": Exclusive_predicted_all_default,
                        f"{resVar}_predicted_all_multiParameter": Exclusive_predicted_all_multiParameter,
                        f"{resVar}_predicted_from_perCall": Exclusive_predicted_perFunc
                        * predicted_call,
                        f"{resVar}_predicted_from_perCall_default": Exclusive_predicted_perFunc_default
                        * predicted_call,
                        f"{resVar}_predicted_from_perCall_multiParameter": Exclusive_predicted_perFunc_multiParameter
                        * predicted_call,
                        "process": elem_process,
                        "iteration": elem_iteration,
                        "size": elem_size,
                    }
                )
                list_series.append(_series)

            resultDF_perFunc: pd.DataFrame = pd.concat(list_series, axis=1).T
            resultDF_perFunc = add_relativeErrorRateCol(
                inputDF=resultDF_perFunc,
                real_colName=f"{resVar}_real_time",
                predicted_colName=f"{resVar}_predicted_all",
                targetColName=f"相対誤差率（Extra-P）",
            )
            resultDF_perFunc = add_relativeErrorRateCol(
                inputDF=resultDF_perFunc,
                real_colName=f"{resVar}_real_time",
                predicted_colName=f"{resVar}_predicted_from_perCall",
                targetColName=f"相対誤差率（組合せ）",
            )
            # print(resultDF_perFunc)
            dict_resultDF_perFunc[
                f"p{elem_process}i{elem_iteration}s{elem_size}"
            ] = resultDF_perFunc

process=512, iteration=256, size=64
process=512, iteration=256, size=96
process=512, iteration=256, size=128
process=512, iteration=512, size=64
process=512, iteration=512, size=96
process=512, iteration=512, size=128
process=512, iteration=1024, size=64
process=512, iteration=1024, size=96
process=512, iteration=1024, size=128
process=729, iteration=256, size=64
process=729, iteration=256, size=96
process=729, iteration=256, size=128
process=729, iteration=512, size=64
process=729, iteration=512, size=96
process=729, iteration=512, size=128
process=729, iteration=1024, size=64
process=729, iteration=1024, size=96
process=729, iteration=1024, size=128
process=1000, iteration=256, size=64
process=1000, iteration=256, size=96
process=1000, iteration=256, size=128
process=1000, iteration=512, size=64
process=1000, iteration=512, size=96
process=1000, iteration=512, size=128
process=1000, iteration=1024, size=64
process=1000, iteration=1024, size=96
process=1000, iteration=1024, size=128


_list_series: list[pd.Series] = []
for key in dict_resultDF_perFunc.keys():
    targetDF = dict_resultDF_perFunc[key]

    relativeErrorOnTarget_perCall: float = ret_relativeError_fromSumOfCol(
        inputDF=targetDF,
        realCol="Exclusive_real_time",
        predictedCol="Exclusive_predicted_from_perCall",
    )
    relativeErrorOnTarget_perCall_default: float = ret_relativeError_fromSumOfCol(
        inputDF=targetDF,
        realCol="Exclusive_real_time",
        predictedCol="Exclusive_predicted_from_perCall_default",
    )
    relativeErrorOnTarget_perCall_multiParameter: float = (
        ret_relativeError_fromSumOfCol(
            inputDF=targetDF,
            realCol="Exclusive_real_time",
            predictedCol="Exclusive_predicted_from_perCall_multiParameter",
        )
    )

    relativeErrorOnTarget_all: float = ret_relativeError_fromSumOfCol(
        inputDF=targetDF,
        realCol="Exclusive_real_time",
        predictedCol="Exclusive_predicted_all",
    )
    relativeErrorOnTarget_all_default: float = ret_relativeError_fromSumOfCol(
        inputDF=targetDF,
        realCol="Exclusive_real_time",
        predictedCol="Exclusive_predicted_all_default",
    )
    relativeErrorOnTarget_all_multiParameter: float = ret_relativeError_fromSumOfCol(
        inputDF=targetDF,
        realCol="Exclusive_real_time",
        predictedCol="Exclusive_predicted_all_multiParameter",
    )

    _series = pd.Series(
        {
            "process": targetDF.reset_index().loc[0]["process"],
            "iteration": targetDF.reset_index().loc[0]["iteration"],
            "size": targetDF.reset_index().loc[0]["size"],
            "Extra-Pのみ": relativeErrorOnTarget_all,
            "Extra-Pのみ（default）": relativeErrorOnTarget_all_default,
            "Extra-Pのみ（multi-parameter）": relativeErrorOnTarget_all_multiParameter,
            "組合せ": relativeErrorOnTarget_perCall,
            "組合せ（default）": relativeErrorOnTarget_perCall_default,
            "組合せ（multi-parameter）": relativeErrorOnTarget_perCall_multiParameter,
            "条件": key,
        }
    )

    _list_series.append(_series)


resultDF: pd.DataFrame = pd.DataFrame(data=_list_series)

resultDF


print(resultDF.to_csv())

,process,iteration,size,Extra-Pのみ,Extra-Pのみ（default）,Extra-Pのみ（multi-parameter）,組合せ,組合せ（default）,組合せ（multi-parameter）,条件
0,512,256,64,12.6333141263911,12.6333141263911,12.6333141263911,0.507208978117047,0.507208978117047,0.507208978117047,p512i256s64
1,512,256,96,8.78762731489746,8.78762731489746,8.78762731489746,1.68957291981639,1.68957291981639,1.68957291981639,p512i256s96
2,512,256,128,9.21255948992760,9.21255948992760,9.21255948992760,0.0990770132824140,0.0990770132824140,0.0990770132824140,p512i256s128
3,512,512,64,13.3301888200060,13.3301888200060,13.3301888200060,0.160422824240916,0.160422824240916,0.160422824240916,p512i512s64
4,512,512,96,9.53668899316149,9.53668899316149,9.53668899316149,0.170231071504648,0.170231071504648,0.170231071504648,p512i512s96
5,512,512,128,8.87409086042504,8.87409086042504,8.87409086042504,0.425618036798869,0.425618036798869,0.425618036798869,p512i512s128
6,512,1024,64,15.7641189708340,15.7641189708340,15.7641189708340,2.82949404518662,2.82949404518662,2.82949404518662,p512i1024s64
7,512,1024,96,8.54672858981889,8.54672858981889,8.54672858981889,0.760886080019178,0.760886080019178,0.760886080019178,p512i1024s96
8,512,1024,128,8.40751501760401,8.40751501760401,8.40751501760401,0.417010936391316,0.417010936391316,0.417010936391316,p512i1024s128
9,729,256,64,14.3130133544459,14.3130133544459,14.3130133544459,3.76154927956209,3.76154927956209,3.76154927956209,p729i256s64
10,729,256,96,7.93703415766320,7.93703415766320,7.93703415766320,2.02957338955345,2.02957338955345,2.02957338955345,p729i256s96
11,729,256,128,8.46494699503024,8.46494699503024,8.46494699503024,0.684945221786042,0.684945221786042,0.684945221786042,p729i256s128
12,729,512,64,14.6495731810092,14.6495731810092,14.6495731810092,0.957466818284560,0.957466818284560,0.957466818284560,p729i512s64
13,729,512,96,9.26813284540945,9.26813284540945,9.26813284540945,0.656547350916434,0.656547350916434,0.656547350916434,p729i512s96
14,729,512,128,8.25169349468960,8.25169349468960,8.25169349468960,0.340345895005132,0.340345895005132,0.340345895005132,p729i512s128
15,729,1024,64,14.8793930637566,14.8793930637566,14.8793930637566,0.0817976553047962,0.0817976553047962,0.0817976553047962,p729i1024s64
16,729,1024,96,9.52582738419136,9.52582738419136,9.52582738419136,0.266803086211699,0.266803086211699,0.266803086211699,p729i1024s96
17,729,1024,128,8.65520760392545,8.65520760392545,8.65520760392545,0.440815082810937,0.440815082810937,0.440815082810937,p729i1024s128
18,1000,256,64,0.406657331497498,0.406657331497498,0.406657331497498,4.00981476929486,4.00981476929486,4.00981476929486,p1000i256s64
19,1000,256,96,7.27377910767353,7.27377910767353,7.27377910767353,0.638296016436708,0.638296016436708,0.638296016436708,p1000i256s96
20,1000,256,128,8.38268605155095,8.38268605155095,8.38268605155095,0.0962293661081201,0.0962293661081201,0.0962293661081201,p1000i256s128
21,1000,512,64,8.78484832879463,8.78484832879463,8.78484832879463,4.47486930123986,4.47486930123986,4.47486930123986,p1000i512s64
22,1000,512,96,9.87695128833714,9.87695128833714,9.87695128833714,0.255622230013760,0.255622230013760,0.255622230013760,p1000i512s96
23,1000,512,128,8.48722370921635,8.48722370921635,8.48722370921635,0.00464939710336938,0.00464939710336938,0.00464939710336938,p1000i512s128
24,1000,1024,64,15.0909877410756,15.0909877410756,15.0909877410756,1.90010926607053,1.90010926607053,1.90010926607053,p1000i1024s64
25,1000,1024,96,9.56472505537316,9.56472505537316,9.56472505537316,0.943611582698895,0.943611582698895,0.943611582698895,p1000i1024s96
26,1000,1024,128,9.17669412006006,9.17669412006006,9.17669412006006,0.692473529391278,0.692473529391278,0.692473529391278,p1000i1024s128


filePath = f"./extra-p_docker/share/input_lulesh_AlltoAll.txt"

# 1. DFの作成
resVar: str = "Exclusive"
list_series_toMakeDF: list[pd.Series] = []
for elem_process in train_lulesh_processes:
    for elem_iteration in train_lulesh_iterations:
        for elem_size in train_lulesh_sizes:
            trainDF_oneCase: pd.DataFrame = ret_averaged_rawDF_lulesh(
                list_process=[elem_process],
                list_iteration=[elem_iteration],
                list_size=[elem_size],
                list_csvDir=list_csvDir,
                resVar=resVar,
            )
            execTime_inThisCase: float = sum(trainDF_oneCase[resVar])
            _series: pd.Series = pd.Series(
                {
                    "process": elem_process,
                    "iteration": elem_iteration,
                    "size": elem_size,
                    resVar: execTime_inThisCase,
                }
            )
            list_series_toMakeDF.append(_series)
inputDFtoExtraP: pd.DataFrame = pd.DataFrame(list_series_toMakeDF)


# 2. Extra-Pへの入力ファイル作成
str_ExtraPinputData: str = gen_ExtraPinputDataFromDF(
    inputDF=inputDFtoExtraP, expVar=expVar, resVar=resVar
)
with open(filePath, mode="w") as f:
    f.write(str_ExtraPinputData)
# 3. Extra-Pの実行
res_str: str = subprocess.run(
    f"extrap --text {filePath} | grep Model",
    stdout=subprocess.PIPE,
    text=True,
    shell=True,
).stdout
res_str_default: str = subprocess.run(
    f"extrap --text {filePath} --modeler default --options poly_exponents=-1,0,1,2,3 log_exponents=0,1 force_combination_exponents=1 allow_negative_exponents=1 | grep Model",
    stdout=subprocess.PIPE,
    text=True,
    shell=True,
).stdout
res_str_multiParameter: str = subprocess.run(
    f"extrap --text {filePath} --modeler multi-parameter | grep Model",
    stdout=subprocess.PIPE,
    text=True,
    shell=True,
).stdout
# 4. 受け取った出力の整形
res_str = res_str.replace("Model: ", "")
res_str = convert_log(res_str)
res_str_default = res_str_default.replace("Model: ", "")
res_str_default = convert_log(res_str_default)
res_str_multiParameter = res_str_multiParameter.replace("Model: ", "")
res_str_multiParameter = convert_log(res_str_multiParameter)

print(f"res_str = {res_str}")
print(f"res_str_default = {res_str_default}")
print(f"res_str_multiParameter = {res_str_multiParameter}")

# 5. モデル化
model_sympy_whole = sympify(res_str, locals=dict_symbols)
model_sympy_whole_default = sympify(res_str_default, locals=dict_symbols)
model_sympy_whole_multiParameter = sympify(res_str_multiParameter, locals=dict_symbols)

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]
Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]

res_str = 		9.933625875783926 + 3.977973652610428e-07 * iteration^(1) * size^(5/2) * 1/ln(2)*ln(size)^(2) + -1.113576890917575 * 1/ln(2)*ln(process)^(1) + -7.601955776753553e-07 * size^(5/2) * 1/ln(2)*ln(size)^(2)

res_str_default = 		9.933625875783926 + 3.977973652610428e-07 * iteration^(1) * size^(5/2) * 1/ln(2)*ln(size)^(2) + -1.113576890917575 * 1/ln(2)*ln(process)^(1) + -7.601955776753553e-07 * size^(5/2) * 1/ln(2)*ln(size)^(2)

res_str_multiParameter = 		9.933625875783926 + 3.977973652610428e-07 * iteration^(1) * size^(5/2) * 1/ln(2)*ln(size)^(2) + -1.113576890917575 * 1/ln(2)*ln(process)^(1) + -7.601955776753553e-07 * size^(5/2) * 1/ln(2)*ln(size)^(2)

Loading file: 100%|██████████| [00:00<00:00, Validating experiment]
Generating models: |          | [00:00<?]/usr/local/lib/python3.10/site-packages/extrap/modelers/single_parameter/basic.py:273: UserWarning: Number of measurements for a parameter needs to be at least 5 in order to create a performance model.
  warnings.warn(
Generating models: 100%|██████████| [00:00<00:00]


print(type(model_sympy_whole))
print(type(model_sympy_whole_default))
print(type(model_sympy_whole_multiParameter))

<class 'sympy.core.add.Add'>
<class 'sympy.core.add.Add'>
<class 'sympy.core.add.Add'>


list_series: list[pd.Series] = []
for elem_process in test_lulesh_processes:
    for elem_iteration in test_lulesh_iterations:
        for elem_size in test_lulesh_sizes:

            print(
                f"process={elem_process}, iteration={elem_iteration}, size={elem_size}"
            )
            condition: str = f"p{elem_process}i{elem_iteration}s{elem_size}"

            # 実時間の取得
            testDF_oneCase: pd.DataFrame = ret_averaged_rawDF_lulesh(
                list_process=[elem_process],
                list_iteration=[elem_iteration],
                list_size=[elem_size],
                list_csvDir=list_csvDir,
                resVar=resVar,
            )
            exclusive_real_time: float = sum(testDF_oneCase[resVar])

            target_env = [
                (dict_symbols["size"], elem_size),
                (dict_symbols["iteration"], elem_iteration),
                (dict_symbols["process"], elem_process),
            ]
            # 総実行時間の予測
            exclusive_predicted_time: float = model_sympy_whole.subs(target_env).evalf()
            exclusive_predicted_time_default: float = model_sympy_whole_default.subs(target_env).evalf()
            exclusive_predicted_time_multiParameter: float = (model_sympy_whole_multiParameter.subs(target_env).evalf())

            _series: pd.Series = pd.Series(
                {
                    "exclusive_real_time": exclusive_real_time,
                    "exclusive_predicted_time": exclusive_predicted_time,
                    "exclusive_predicted_time_default": exclusive_predicted_time_default,
                    "exclusive_predicted_time_multiParameter": exclusive_predicted_time_multiParameter,
                    "条件": condition,
                }
            )

            list_series.append(_series)

resultDF2: pd.DataFrame = pd.DataFrame(list_series)
resultDF2 = add_relativeErrorRateCol(
    inputDF=resultDF2,
    real_colName="exclusive_real_time",
    predicted_colName="exclusive_predicted_time",
    targetColName="Extra-P単体による全体予測",
)
resultDF2 = add_relativeErrorRateCol(
    inputDF=resultDF2,
    real_colName="exclusive_real_time",
    predicted_colName="exclusive_predicted_time_default",
    targetColName="Extra-P単体による全体予測（default）",
)
resultDF2 = add_relativeErrorRateCol(
    inputDF=resultDF2,
    real_colName="exclusive_real_time",
    predicted_colName="exclusive_predicted_time_multiParameter",
    targetColName="Extra-P単体による全体予測（multi-parameter）",
)

resultDF2

process=512, iteration=256, size=64
process=512, iteration=256, size=96
process=512, iteration=256, size=128
process=512, iteration=512, size=64
process=512, iteration=512, size=96
process=512, iteration=512, size=128
process=512, iteration=1024, size=64
process=512, iteration=1024, size=96
process=512, iteration=1024, size=128
process=729, iteration=256, size=64
process=729, iteration=256, size=96
process=729, iteration=256, size=128
process=729, iteration=512, size=64
process=729, iteration=512, size=96
process=729, iteration=512, size=128
process=729, iteration=1024, size=64
process=729, iteration=1024, size=96
process=729, iteration=1024, size=128
process=1000, iteration=256, size=64
process=1000, iteration=256, size=96
process=1000, iteration=256, size=128
process=1000, iteration=512, size=64
process=1000, iteration=512, size=96
process=1000, iteration=512, size=128
process=1000, iteration=1024, size=64
process=1000, iteration=1024, size=96
process=1000, iteration=1024, size=128


mergedDF: pd.DataFrame = pd.merge(
    left=resultDF,
    right=resultDF2.drop(columns=["exclusive_real_time", "exclusive_predicted_time", "exclusive_predicted_time_default", "exclusive_predicted_time_multiParameter"]),
    on="条件",
)
print(mergedDF.to_csv())

,process,iteration,size,Extra-Pのみ,Extra-Pのみ（default）,Extra-Pのみ（multi-parameter）,組合せ,組合せ（default）,組合せ（multi-parameter）,条件,Extra-P単体による全体予測,Extra-P単体による全体予測（default）,Extra-P単体による全体予測（multi-parameter）
0,512,256,64,12.6333141263911,12.6333141263911,12.6333141263911,0.507208978117047,0.507208978117047,0.507208978117047,p512i256s64,37.5266080780388,37.5266080780388,37.5266080780388
1,512,256,96,8.78762731489746,8.78762731489746,8.78762731489746,1.68957291981639,1.68957291981639,1.68957291981639,p512i256s96,41.8980578293707,41.8980578293707,41.8980578293707
2,512,256,128,9.21255948992760,9.21255948992760,9.21255948992760,0.0990770132824140,0.0990770132824140,0.0990770132824140,p512i256s128,44.3735302064036,44.3735302064036,44.3735302064036
3,512,512,64,13.3301888200060,13.3301888200060,13.3301888200060,0.160422824240916,0.160422824240916,0.160422824240916,p512i512s64,37.0291935881498,37.0291935881498,37.0291935881498
4,512,512,96,9.53668899316149,9.53668899316149,9.53668899316149,0.170231071504648,0.170231071504648,0.170231071504648,p512i512s96,42.3896327974435,42.3896327974435,42.3896327974435
5,512,512,128,8.87409086042504,8.87409086042504,8.87409086042504,0.425618036798869,0.425618036798869,0.425618036798869,p512i512s128,44.3686915433420,44.3686915433420,44.3686915433420
6,512,1024,64,15.7641189708340,15.7641189708340,15.7641189708340,2.82949404518662,2.82949404518662,2.82949404518662,p512i1024s64,38.0027532959863,38.0027532959863,38.0027532959863
7,512,1024,96,8.54672858981889,8.54672858981889,8.54672858981889,0.760886080019178,0.760886080019178,0.760886080019178,p512i1024s96,41.6412333065201,41.6412333065201,41.6412333065201
8,512,1024,128,8.40751501760401,8.40751501760401,8.40751501760401,0.417010936391316,0.417010936391316,0.417010936391316,p512i1024s128,44.0978181335054,44.0978181335054,44.0978181335054
9,729,256,64,14.3130133544459,14.3130133544459,14.3130133544459,3.76154927956209,3.76154927956209,3.76154927956209,p729i256s64,42.6915098023799,42.6915098023799,42.6915098023799
10,729,256,96,7.93703415766320,7.93703415766320,7.93703415766320,2.02957338955345,2.02957338955345,2.02957338955345,p729i256s96,42.4680392159481,42.4680392159481,42.4680392159481
11,729,256,128,8.46494699503024,8.46494699503024,8.46494699503024,0.684945221786042,0.684945221786042,0.684945221786042,p729i256s128,44.3794929769896,44.3794929769896,44.3794929769896
12,729,512,64,14.6495731810092,14.6495731810092,14.6495731810092,0.957466818284560,0.957466818284560,0.957466818284560,p729i512s64,40.1699403867848,40.1699403867848,40.1699403867848
13,729,512,96,9.26813284540945,9.26813284540945,9.26813284540945,0.656547350916434,0.656547350916434,0.656547350916434,p729i512s96,42.8138711596887,42.8138711596887,42.8138711596887
14,729,512,128,8.25169349468960,8.25169349468960,8.25169349468960,0.340345895005132,0.340345895005132,0.340345895005132,p729i512s128,44.2474877493777,44.2474877493777,44.2474877493777
15,729,1024,64,14.8793930637566,14.8793930637566,14.8793930637566,0.0817976553047962,0.0817976553047962,0.0817976553047962,p729i1024s64,38.5848336225292,38.5848336225292,38.5848336225292
16,729,1024,96,9.52582738419136,9.52582738419136,9.52582738419136,0.266803086211699,0.266803086211699,0.266803086211699,p729i1024s96,42.6109031659102,42.6109031659102,42.6109031659102
17,729,1024,128,8.65520760392545,8.65520760392545,8.65520760392545,0.440815082810937,0.440815082810937,0.440815082810937,p729i1024s128,44.4059120984211,44.4059120984211,44.4059120984211
18,1000,256,64,0.406657331497498,0.406657331497498,0.406657331497498,4.00981476929486,4.00981476929486,4.00981476929486,p1000i256s64,41.8342246445247,41.8342246445247,41.8342246445247
19,1000,256,96,7.27377910767353,7.27377910767353,7.27377910767353,0.638296016436708,0.638296016436708,0.638296016436708,p1000i256s96,44.3434531931994,44.3434531931994,44.3434531931994
20,1000,256,128,8.38268605155095,8.38268605155095,8.38268605155095,0.0962293661081201,0.0962293661081201,0.0962293661081201,p1000i256s128,45.2868933647090,45.2868933647090,45.2868933647090
21,1000,512,64,8.78484832879463,8.78484832879463,8.78484832879463,4.47486930123986,4.47486930123986,4.47486930123986,p1000i512s64,40.6047982824067,40.6047982824067,40.6047982824067
22,1000,512,96,9.87695128833714,9.87695128833714,9.87695128833714,0.255622230013760,0.255622230013760,0.255622230013760,p1000i512s96,44.3835692477177,44.3835692477177,44.3835692477177
23,1000,512,128,8.48722370921635,8.48722370921635,8.48722370921635,0.00464939710336938,0.00464939710336938,0.00464939710336938,p1000i512s128,44.8951005291249,44.8951005291249,44.8951005291249
24,1000,1024,64,15.0909877410756,15.0909877410756,15.0909877410756,1.90010926607053,1.90010926607053,1.90010926607053,p1000i1024s64,41.1291557213599,41.1291557213599,41.1291557213599
25,1000,1024,96,9.56472505537316,9.56472505537316,9.56472505537316,0.943611582698895,0.943611582698895,0.943611582698895,p1000i1024s96,43.2830043373717,43.2830043373717,43.2830043373717
26,1000,1024,128,9.17669412006006,9.17669412006006,9.17669412006006,0.692473529391278,0.692473529391278,0.692473529391278,p1000i1024s128,44.9997517286953,44.9997517286953,44.9997517286953


mergedDF


# 実時間の取得
trainDF_oneCase: pd.DataFrame = ret_averaged_rawDF_lulesh(
    list_process=[train_lulesh_processes[-1]],
    list_iteration=[train_lulesh_iterations[-1]],
    list_size=[train_lulesh_sizes[-1]],
    list_csvDir=list_csvDir,
    resVar=resVar,
)
exclusive_real_time: float = sum(trainDF_oneCase[resVar])

target_env = [
    (dict_symbols["size"], train_lulesh_sizes[-1]),
    (dict_symbols["iteration"], train_lulesh_iterations[-1]),
    (dict_symbols["process"], train_lulesh_processes[-1]),
]
# 総実行時間の予測
exclusive_predicted_time: float = model_sympy_whole.subs(target_env).evalf()

_series: pd.Series = pd.Series(
    {
        "exclusive_real_time": exclusive_real_time,
        "exclusive_predicted_time": exclusive_predicted_time,
        "条件": condition,
    }
)

_series

exclusive_real_time                26.232715
exclusive_predicted_time    17.8654504325857
条件                            p1000i1024s128
dtype: object


print(train_lulesh_processes[-1])
print(train_lulesh_iterations[-1])
print(train_lulesh_sizes[-1])

343
128
48


_process = 64
_iteration = 64
_size = 24

# 実時間の取得
trainDF_oneCase: pd.DataFrame = ret_averaged_rawDF_lulesh(
    list_process=[_process],
    list_iteration=[_iteration],
    list_size=[_size],
    list_csvDir=list_csvDir,
    resVar=resVar,
)
exclusive_real_time: float = sum(trainDF_oneCase[resVar])

target_env = [
    (dict_symbols["size"], _size),
    (dict_symbols["iteration"], _iteration),
    (dict_symbols["process"], _process),
]
# 総実行時間の予測
exclusive_predicted_time: float = model_sympy_whole.subs(target_env).evalf()

_series: pd.Series = pd.Series(
    {
        "exclusive_real_time": exclusive_real_time,
        "exclusive_predicted_time": exclusive_predicted_time,
        # "条件" :condition
    }
)

_series

exclusive_real_time                 3.383667
exclusive_predicted_time    4.26771526403966
dtype: object


# 1. ✅モデル構築（＜－上の方で作成されたモデルを使う）
# 2. ✅各条件下で1つのDFをループで回しつつ作成
# 3. ✅条件が絞られたDFで関数ごとにループを回す
# 4. ✅予測を行う
# 5. ✅目標のテーブル形式にする

resVar = "Exclusive"

_list_series: list[pd.Series] = []
for elem_process in test_lulesh_processes:
    for elem_iteration in test_lulesh_iterations:
        for elem_size in test_lulesh_sizes:
            targetDF_oneCondition: pd.DataFrame = ret_averaged_rawDF_lulesh(
                list_process=[elem_process],
                list_iteration=[elem_iteration],
                list_size=[elem_size],
                list_csvDir=list_csvDir,
                resVar=resVar,
            )

            for functionName in functionNames:

                targetDF_oneCondition_perCall: pd.DataFrame = targetDF_oneCondition[
                    targetDF_oneCondition["Name"] == functionName
                ]

                if len(targetDF_oneCondition_perCall) != 1:
                    warnings.warn("len(targetDF_oneCondition_perCall) != 1")
                    exit

                predicted_call: float = float(
                    np.array(
                        dict_models[functionName]["call_perFunc"].predict(
                            inputDF=targetDF_oneCondition_perCall[expVar]
                        )
                    )
                )
                real_call: float = targetDF_oneCondition_perCall.reset_index().loc[0][
                    "#Call"
                ]
                _list_series.append(
                    pd.Series(
                        {
                            "predicted_call": predicted_call,
                            "real_call": real_call,
                            "functionName": functionName,
                            "process": elem_process,
                            "iteration": elem_iteration,
                            "size": elem_size,
                        }
                    )
                )

resultDF_aboutCall: pd.DataFrame = pd.DataFrame(data=_list_series)
resultDF_aboutCall = add_relativeErrorRateCol(
    inputDF=resultDF_aboutCall,
    real_colName="real_call",
    predicted_colName="predicted_call",
    targetColName="absolute_relative_error",
)


resultDF_aboutCall


_list_series: [pd.Series] = []
for elem_process in test_lulesh_processes:
    for elem_iteration in test_lulesh_iterations:
        for elem_size in test_lulesh_sizes:
            resultDF_aboutCall_oneCondition: pd.DataFrame = resultDF_aboutCall[
                (resultDF_aboutCall["process"] == elem_process)
                & (resultDF_aboutCall["iteration"] == elem_iteration)
                & (resultDF_aboutCall["size"] == elem_size)
            ]
            _list_series.append(
                pd.Series(
                    {
                        "process": elem_process,
                        "iteration": elem_iteration,
                        "size": elem_size,
                        "MAPE": resultDF_aboutCall_oneCondition.mean()[
                            "absolute_relative_error"
                        ],
                    }
                )
            )

resultDF_aboutCall_withMAPE: pd.DataFrame = pd.DataFrame(data=_list_series)
resultDF_aboutCall_withMAPE

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

/tmp/ipykernel_11192/3753474081.py:16: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.


DF_pis_time_at_target


# resultDF_aboutCall_withMAPE
# DF_pis_time_at_target

objective_DF: pd.DataFrame = pd.merge(
    resultDF_aboutCall_withMAPE,
    DF_pis_time_at_target,
    how="outer",
    left_on=["process", "iteration", "size"],
    right_on=["p", "i", "s"],
).drop(columns=["p", "i", "s", "time"])


objective_DF["relative_cost"] = costToBuildModel / objective_DF["cost"]


objective_DF = objective_DF.drop(columns=["cost"])


condition_col: list[str] = ["process", "iteration", "size"]
objective_DF = objective_DF.groupby(condition_col).aggregate(np.mean)

objective_DF


print(objective_DF.to_csv())

process,iteration,size,MAPE,relative_cost
512.0,256.0,64.0,0.9935228424782739,1.1955379691207877
512.0,256.0,96.0,1.0017129535328058,0.3306807148631966
512.0,256.0,128.0,1.01102184985611,0.1412908508960931
512.0,512.0,64.0,1.0970138085403747,0.5903891205534754
512.0,512.0,96.0,1.0159282298637726,0.1630318772949437
512.0,512.0,128.0,0.9871075506651344,0.06757388521117497
512.0,1024.0,64.0,1.5194043925948364,0.28962485159227097
512.0,1024.0,96.0,1.2072433542690577,0.08179996630826443
512.0,1024.0,128.0,1.0962401664788017,0.034037216254517756
729.0,256.0,64.0,2.0946356523227534,0.7623620784910629
729.0,256.0,96.0,2.0836302318077387,0.23063480433074038
729.0,256.0,128.0,2.0977683231348347,0.09923308046474577
729.0,512.0,64.0,2.1881172819131347,0.39947002850226143
729.0,512.0,96.0,2.1012511001702263,0.11370457136585453
729.0,512.0,128.0,2.0702972079954542,0.04745929935270451
729.0,1024.0,64.0,2.5989122751640394,0.20218769038554188
729.0,1024.0,96.0,2.2896535946804377,0.05745073079537913
729.0,1024.0,128.0,2.178993503376056,0.023729649676352254
1000.0,256.0,64.0,3.561134026442501,0.5829670858950888
1000.0,256.0,96.0,3.4869652443523176,0.1635257376357504
1000.0,256.0,128.0,3.5091560289523542,0.06832197589997746
1000.0,512.0,64.0,3.627803425126743,0.28794702486580537
1000.0,512.0,96.0,3.5248944816066228,0.08065219243894899
1000.0,512.0,128.0,3.4882787904946517,0.03459782922812158
1000.0,1024.0,64.0,4.016988691058634,0.1421284447790995
1000.0,1024.0,96.0,3.706513299954468,0.04118355637066753
1000.0,1024.0,128.0,3.5946778500094716,0.01705353993868404


_list_series: list[pd.Series] = []
for i, sr in trainDF.iterrows():
    functionName = sr["Name"]
    DF_to_predict: pd.DataFrame = pd.DataFrame(sr).T[expVar]
    DF_to_predict = DF_to_predict.astype(
        {"process": "int32", "iteration": "int32", "size": "int32"}
    )

    predicted_call: float = float(
        dict_models[functionName]["call_perFunc"].predict(inputDF=DF_to_predict)
    )

    real_call: float = sr["#Call"]

    _list_series.append(
        pd.Series(
            {
                "process": sr["process"],
                "iteration": sr["iteration"],
                "size": sr["size"],
                "predicted_call": predicted_call,
                "real_call": real_call,
            }
        )
    )
DF_result_lulesh_about_call: pd.DataFrame = pd.DataFrame(_list_series)
DF_result_lulesh_about_call = add_relativeErrorRateCol(
    inputDF=DF_result_lulesh_about_call,
    real_colName="real_call",
    predicted_colName="predicted_call",
    targetColName="relative_error_rate",
)


DF_result_lulesh_about_call


DF_result_lulesh_about_call.mean()

process                  130.500000
iteration                 49.600000
size                      30.000000
predicted_call         63918.435869
real_call              63915.372350
relative_error_rate        0.475022
dtype: float64


_list_series: list[pd.Series] = []
for elem_process in train_lulesh_processes:
    for elem_iteration in train_lulesh_iterations:
        for elem_size in train_lulesh_sizes:
            for functionName in functionNames:
                trainDF_oneCondition: pd.DataFrame = trainDF[
                    (trainDF["Name"] == functionName)
                    & (trainDF["process"] == elem_process)
                    & (trainDF["iteration"] == elem_iteration)
                    & (trainDF["size"] == elem_size)
                ]

                predicted_call: float = float(
                    np.array(
                        dict_models[functionName]["call_perFunc"].predict(
                            inputDF=trainDF_oneCondition[expVar]
                        )
                    )
                )
                real_call: float = trainDF.reset_index().loc[0]["#Call"]

                _list_series.append(
                    pd.Series(
                        {
                            "process": elem_process,
                            "iteration": elem_iteration,
                            "size": elem_size,
                            "functionName": functionName,
                            "predicted_call": predicted_call,
                            "real_call": real_call,
                        }
                    )
                )

DF_result_lulesh_about_call: pd.DataFrame = pd.DataFrame(_list_series)
DF_result_lulesh_about_call = add_relativeErrorRateCol(
    inputDF=DF_result_lulesh_about_call,
    real_colName="real_call",
    predicted_colName="predicted_call",
    targetColName="relative_error_rate",
)
DF_result_lulesh_about_call


trainDF.reset_index()


DF_result_lulesh_about_call.mean()

/tmp/ipykernel_11192/3814533107.py:1: FutureWarning:

The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.

process                1.305000e+02
iteration              4.960000e+01
size                   3.000000e+01
predicted_call         6.391537e+04
real_call              1.000000e+00
relative_error_rate    6.391443e+06
dtype: float64

	p	i	s	time	cost
0	8.0	8.0	16.0	0.044	0.352
30	8.0	8.0	16.0	0.044	0.352
60	8.0	8.0	16.0	0.044	0.352
1	8.0	8.0	24.0	0.170	1.360
31	8.0	8.0	24.0	0.170	1.360
...	...	...	...	...	...
446	343.0	128.0	32.0	8.200	2812.600
476	343.0	128.0	32.0	7.500	2572.500
417	343.0	128.0	48.0	25.000	8575.000
447	343.0	128.0	48.0	27.000	9261.000
477	343.0	128.0	48.0	24.000	8232.000

	level_0	index	%Time	Exclusive	Inclusive	#Call	#Subrs	Name	process	iteration	size	ExclusivePerCall	functionName
0	0	0	100.0	3.420000e-06	4,647	1.000000	1.000	.TAU_application	8	8	16	3.500000e-06	.TAU_application
1	1	1	100.0	2.500000e-02	4,647	1.000000	100.125	int_main(int_char_**)	8	8	16	2.500000e-02	int_main(int_char_**)
2	2	2	84.0	6.192000e+00	3,901	1.000000	0.000	MPI_Finalize()	8	8	16	3.901000e+00	MPI_Finalize()
3	3	3	15.1	7.276667e-01	699	1.000000	0.000	MPI_Init()	8	8	16	6.990000e-01	MPI_Init()
4	4	4	0.2	6.000000e-03	11	8.000000	32768.000	void_CalcKinematicsForElems(Domain	8	8	16	7.500000e-04	void_CalcKinematicsForElems(Domain
...	...	...	...	...	...	...	...	...	...	...	...	...	...
3835	3835	27	0.0	5.483333e-06	0.00538	1.000000	0.000	void_Domain::SetupSymmetryPlanes(Int_t)	343	128	48	5.380000e-06	void_Domain::SetupSymmetryPlanes(Int_t)
3836	3836	28	0.0	1.606667e-06	0.00355	1.000000	2.000	void_ParseCommandLineOptions(int_char_**_Int_t...	343	128	48	1.620000e-06	void_ParseCommandLineOptions(int_char_**_Int_t...
3837	3837	29	0.0	1.896667e-06	0.00193	2.000000	0.000	StrToInt	343	128	48	9.650000e-07	StrToInt
3838	3838	30	0.0	5.170000e-07	0.000478	1.000000	0.000	MPI_Comm_size()	343	128	48	4.780000e-07	MPI_Comm_size()
3839	3839	31	0.0	2.703333e-07	0.000254	0.002915	0.000	void_VerifyAndWriteFinalOutput(Real_t_Domain	343	128	48	8.712206e-05	void_VerifyAndWriteFinalOutput(Real_t_Domain

TODO¶

Exclusive¶

総実行時間から総実行時間を予測する¶

表を作成する¶

モデルに対する誤差率のMAPEを算出する¶

	p	i	s	time	cost
480	512.0	256.0	64.0	130.0	66560.0
489	512.0	256.0	64.0	130.0	66560.0
498	512.0	256.0	64.0	130.0	66560.0
481	512.0	256.0	96.0	470.0	240640.0
490	512.0	256.0	96.0	470.0	240640.0
...	...	...	...	...	...
550	1000.0	1024.0	96.0	1900.0	1900000.0
559	1000.0	1024.0	96.0	2000.0	2000000.0
542	1000.0	1024.0	128.0	4600.0	4600000.0
551	1000.0	1024.0	128.0	4700.0	4700000.0
560	1000.0	1024.0	128.0	4700.0	4700000.0

	cost	process	iteration	size	relativeCost(=costToBuildModel/cost * 100)
0	6.766045e+04	512.0	256.0	64.0	117.609342
1	2.416557e+05	512.0	256.0	96.0	32.929089
2	5.856285e+05	512.0	256.0	128.0	13.587966
3	1.348294e+05	512.0	512.0	64.0	59.019038
4	4.893476e+05	512.0	512.0	96.0	16.261448
5	1.175641e+06	512.0	512.0	128.0	6.768646
6	2.744798e+05	512.0	1024.0	64.0	28.991209
7	9.680321e+05	512.0	1024.0	96.0	8.220286
8	2.344354e+06	512.0	1024.0	128.0	3.394325
9	1.042970e+05	729.0	256.0	64.0	76.296510
10	3.467657e+05	729.0	256.0	96.0	22.947777
11	8.331797e+05	729.0	256.0	128.0	9.550761
12	2.013598e+05	729.0	512.0	64.0	39.518824
13	7.011921e+05	729.0	512.0	96.0	11.348532
14	1.669530e+06	729.0	512.0	128.0	4.766312
15	3.938422e+05	729.0	1024.0	64.0	20.204793
16	1.400879e+06	729.0	1024.0	96.0	5.680364
17	3.355712e+06	729.0	1024.0	128.0	2.371330
18	1.400870e+05	1000.0	256.0	64.0	56.803996
19	4.907890e+05	1000.0	256.0	96.0	16.213689
20	1.160934e+06	1000.0	256.0	128.0	6.854393
21	2.773810e+05	1000.0	512.0	64.0	28.687982
22	9.880887e+05	1000.0	512.0	96.0	8.053427
23	2.316158e+06	1000.0	512.0	128.0	3.435647
24	5.627363e+05	1000.0	1024.0	64.0	14.140727
25	1.943521e+06	1000.0	1024.0	96.0	4.094374
26	4.651948e+06	1000.0	1024.0	128.0	1.710574

	process	iteration	size	Extra-Pのみ	Extra-Pのみ（default）	Extra-Pのみ（multi-parameter）	組合せ	組合せ（default）	組合せ（multi-parameter）	条件
0	512	256	64	12.6333141263911	12.6333141263911	12.6333141263911	0.507208978117047	0.507208978117047	0.507208978117047	p512i256s64
1	512	256	96	8.78762731489746	8.78762731489746	8.78762731489746	1.68957291981639	1.68957291981639	1.68957291981639	p512i256s96
2	512	256	128	9.21255948992760	9.21255948992760	9.21255948992760	0.0990770132824140	0.0990770132824140	0.0990770132824140	p512i256s128
3	512	512	64	13.3301888200060	13.3301888200060	13.3301888200060	0.160422824240916	0.160422824240916	0.160422824240916	p512i512s64
4	512	512	96	9.53668899316149	9.53668899316149	9.53668899316149	0.170231071504648	0.170231071504648	0.170231071504648	p512i512s96
5	512	512	128	8.87409086042504	8.87409086042504	8.87409086042504	0.425618036798869	0.425618036798869	0.425618036798869	p512i512s128
6	512	1024	64	15.7641189708340	15.7641189708340	15.7641189708340	2.82949404518662	2.82949404518662	2.82949404518662	p512i1024s64
7	512	1024	96	8.54672858981889	8.54672858981889	8.54672858981889	0.760886080019178	0.760886080019178	0.760886080019178	p512i1024s96
8	512	1024	128	8.40751501760401	8.40751501760401	8.40751501760401	0.417010936391316	0.417010936391316	0.417010936391316	p512i1024s128
9	729	256	64	14.3130133544459	14.3130133544459	14.3130133544459	3.76154927956209	3.76154927956209	3.76154927956209	p729i256s64
10	729	256	96	7.93703415766320	7.93703415766320	7.93703415766320	2.02957338955345	2.02957338955345	2.02957338955345	p729i256s96
11	729	256	128	8.46494699503024	8.46494699503024	8.46494699503024	0.684945221786042	0.684945221786042	0.684945221786042	p729i256s128
12	729	512	64	14.6495731810092	14.6495731810092	14.6495731810092	0.957466818284560	0.957466818284560	0.957466818284560	p729i512s64
13	729	512	96	9.26813284540945	9.26813284540945	9.26813284540945	0.656547350916434	0.656547350916434	0.656547350916434	p729i512s96
14	729	512	128	8.25169349468960	8.25169349468960	8.25169349468960	0.340345895005132	0.340345895005132	0.340345895005132	p729i512s128
15	729	1024	64	14.8793930637566	14.8793930637566	14.8793930637566	0.0817976553047962	0.0817976553047962	0.0817976553047962	p729i1024s64
16	729	1024	96	9.52582738419136	9.52582738419136	9.52582738419136	0.266803086211699	0.266803086211699	0.266803086211699	p729i1024s96
17	729	1024	128	8.65520760392545	8.65520760392545	8.65520760392545	0.440815082810937	0.440815082810937	0.440815082810937	p729i1024s128
18	1000	256	64	0.406657331497498	0.406657331497498	0.406657331497498	4.00981476929486	4.00981476929486	4.00981476929486	p1000i256s64
19	1000	256	96	7.27377910767353	7.27377910767353	7.27377910767353	0.638296016436708	0.638296016436708	0.638296016436708	p1000i256s96
20	1000	256	128	8.38268605155095	8.38268605155095	8.38268605155095	0.0962293661081201	0.0962293661081201	0.0962293661081201	p1000i256s128
21	1000	512	64	8.78484832879463	8.78484832879463	8.78484832879463	4.47486930123986	4.47486930123986	4.47486930123986	p1000i512s64
22	1000	512	96	9.87695128833714	9.87695128833714	9.87695128833714	0.255622230013760	0.255622230013760	0.255622230013760	p1000i512s96
23	1000	512	128	8.48722370921635	8.48722370921635	8.48722370921635	0.00464939710336938	0.00464939710336938	0.00464939710336938	p1000i512s128
24	1000	1024	64	15.0909877410756	15.0909877410756	15.0909877410756	1.90010926607053	1.90010926607053	1.90010926607053	p1000i1024s64
25	1000	1024	96	9.56472505537316	9.56472505537316	9.56472505537316	0.943611582698895	0.943611582698895	0.943611582698895	p1000i1024s96
26	1000	1024	128	9.17669412006006	9.17669412006006	9.17669412006006	0.692473529391278	0.692473529391278	0.692473529391278	p1000i1024s128

	exclusive_real_time	exclusive_predicted_time	exclusive_predicted_time_default	exclusive_predicted_time_multiParameter	条件	Extra-P単体による全体予測	Extra-P単体による全体予測（default）	Extra-P単体による全体予測（multi-parameter）
0	132.149312	82.5581576693674	82.5581576693674	82.5581576693674	p512i256s64	37.5266080780388	37.5266080780388	37.5266080780388
1	471.983715	274.231705131693	274.231705131693	274.231705131693	p512i256s96	41.8980578293707	41.8980578293707	41.8980578293707
2	1143.805716	636.258741337536	636.258741337536	636.258741337536	p512i256s128	44.3735302064036	44.3735302064036	44.3735302064036
3	263.338648	165.826470359938	165.826470359938	165.826470359938	p512i512s64	37.0291935881498	37.0291935881498	37.0291935881498
4	955.757055	550.615148688673	550.615148688673	550.615148688673	p512i512s96	42.3896327974435	42.3896327974435	42.3896327974435
5	2296.174716	1277.39203900681	1277.39203900681	1277.39203900681	p512i512s128	44.3686915433420	44.3686915433420	44.3686915433420
6	536.093316	332.363095741079	332.363095741079	332.363095741079	p512i1024s64	38.0027532959863	38.0027532959863	38.0027532959863
7	1890.687721	1103.38203580263	1103.38203580263	1103.38203580263	p512i1024s96	41.6412333065201	41.6412333065201	41.6412333065201
8	4578.817049	2559.65863434536	2559.65863434536	2559.65863434536	p512i1024s128	44.0978181335054	44.0978181335054	44.0978181335054
9	143.068651	81.9904840049816	81.9904840049816	81.9904840049816	p729i256s64	42.6915098023799	42.6915098023799	42.6915098023799
10	475.673048	273.664031467307	273.664031467307	273.664031467307	p729i256s96	42.4680392159481	42.4680392159481	42.4680392159481
11	1142.907718	635.691067673150	635.691067673150	635.691067673150	p729i256s128	44.3794929769896	44.3794929769896	44.3794929769896
12	276.213659	165.258796695552	165.258796695552	165.258796695552	p729i512s64	40.1699403867848	40.1699403867848	40.1699403867848
13	961.854712	550.047475024287	550.047475024287	550.047475024287	p729i512s96	42.8138711596887	42.8138711596887	42.8138711596887
14	2290.164719	1276.82436534243	1276.82436534243	1276.82436534243	p729i512s128	44.2474877493777	44.2474877493777	44.2474877493777
15	540.249977	331.795422076693	331.795422076693	331.795422076693	p729i1024s64	38.5848336225292	38.5848336225292	38.5848336225292
16	1921.644394	1102.81436213825	1102.81436213825	1102.81436213825	p729i1024s96	42.6109031659102	42.6109031659102	42.6109031659102
17	4603.171052	2559.09096068098	2559.09096068098	2559.09096068098	p729i1024s128	44.4059120984211	44.4059120984211	44.4059120984211
18	140.086988	81.4826826083564	81.4826826083564	81.4826826083564	p1000i256s64	41.8342246445247	41.8342246445247	41.8342246445247
19	490.789037	273.156230070682	273.156230070682	273.156230070682	p1000i256s96	44.3434531931994	44.3434531931994	44.3434531931994
20	1160.934382	635.183266276524	635.183266276524	635.183266276524	p1000i256s128	45.2868933647090	45.2868933647090	45.2868933647090
21	277.380985	164.750995298927	164.750995298927	164.750995298927	p1000i512s64	40.6047982824067	40.6047982824067	40.6047982824067
22	988.088711	549.539673627662	549.539673627662	549.539673627662	p1000i512s96	44.3835692477177	44.3835692477177	44.3835692477177
23	2316.158048	1276.31656394580	1276.31656394580	1276.31656394580	p1000i512s128	44.8951005291249	44.8951005291249	44.8951005291249
24	562.736317	331.287620680068	331.287620680068	331.287620680068	p1000i1024s64	41.1291557213599	41.1291557213599	41.1291557213599
25	1943.520717	1102.30656074162	1102.30656074162	1102.30656074162	p1000i1024s96	43.2830043373717	43.2830043373717	43.2830043373717
26	4651.948382	2558.58315928435	2558.58315928435	2558.58315928435	p1000i1024s128	44.9997517286953	44.9997517286953	44.9997517286953

	process	iteration	size	functionName	predicted_call	real_call	relative_error_rate
0	8	8	16	.TAU_application	1.000000	1.0	0.000000
1	8	8	16	MPI_Allreduce()	7.000000	1.0	600.000000
2	8	8	16	MPI_Barrier()	1.000000	1.0	0.000000
3	8	8	16	MPI_Comm_rank()	77.000000	1.0	7600.000000
4	8	8	16	MPI_Comm_size()	1.000000	1.0	0.000000
...	...	...	...	...	...	...	...
3835	343	128	48	void_Domain::SetupSymmetryPlanes(Int_t)	1.000000	1.0	0.000000
3836	343	128	48	void_Domain::~Domain()	1.000000	1.0	0.000000
3837	343	128	48	void_InitMeshDecomp(Int_t_Int_t_Int_t	1.000000	1.0	0.000000
3838	343	128	48	void_ParseCommandLineOptions(int_char_**_Int_t...	1.000000	1.0	0.000000
3839	343	128	48	void_VerifyAndWriteFinalOutput(Real_t_Domain	0.002915	1.0	99.708455

			MAPE	relative_cost
process	iteration	size
512.0	256.0	64.0	0.993523	1.195538
		96.0	1.001713	0.330681
		128.0	1.011022	0.141291
	512.0	64.0	1.097014	0.590389
		96.0	1.015928	0.163032
		128.0	0.987108	0.067574
	1024.0	64.0	1.519404	0.289625
		96.0	1.207243	0.081800
		128.0	1.096240	0.034037
729.0	256.0	64.0	2.094636	0.762362
		96.0	2.083630	0.230635
		128.0	2.097768	0.099233
	512.0	64.0	2.188117	0.399470
		96.0	2.101251	0.113705
		128.0	2.070297	0.047459
	1024.0	64.0	2.598912	0.202188
		96.0	2.289654	0.057451
		128.0	2.178994	0.023730
1000.0	256.0	64.0	3.561134	0.582967
		96.0	3.486965	0.163526
		128.0	3.509156	0.068322
	512.0	64.0	3.627803	0.287947
		96.0	3.524894	0.080652
		128.0	3.488279	0.034598
	1024.0	64.0	4.016989	0.142128
		96.0	3.706513	0.041184
		128.0	3.594678	0.017054